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期刊名称: Acta Numerica
Volume:24    Page:259-328
ISSN:0962-4929

Multilevel Monte Carlo methods期刊论文

作者: Giles Michael B
DOI:10.1017/S096249291500001X

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页码: 259-328
被引频次: 118
出版者: CAMBRIDGE UNIV PRESS,Cambridge University Press
期刊名称: Acta Numerica
ISSN: 0962-4929
语言: English
摘要: Monte Carlo methods are a very general and useful approach for the estimation of expectations arising from stochastic simulation. However, they can be computationally expensive, particularly when the cost of generating individual stochastic samples is very high, as in the case of stochastic PDEs. Multilevel Monte Carlo is a recently developed approach which greatly reduces the computational cost by performing most simulations with low accuracy at a correspondingly low cost, with relatively few simulations being performed at high accuracy and a high cost. In this article, we review the ideas behind the multilevel Monte Carlo method, and various recent generalizations and extensions, and discuss a number of applications which illustrate the flexibility and generality of the approach and the challenges in developing more efficient implementations with a faster rate of convergence of the multilevel correction variance.
相关主题: MATHEMATICS, FUNCTION-SPACES, DIFFUSION-PROCESSES, APPROXIMATION, PARTIAL-DIFFERENTIAL-EQUATIONS, COMPLEXITY, INFINITE-DIMENSIONAL INTEGRATION, SDES, ALGORITHMS, SIMULATION, ELLIPTIC PDES, Monte Carlo simulation,

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